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Convergence rates of kernel density estimates in particle filtering
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SYSNO ASEP 0506808 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Convergence rates of kernel density estimates in particle filtering Author(s) Coufal, David (UIVT-O) RID, SAI, ORCID Source Title Statistics & Probability Letters. - : Elsevier - ISSN 0167-7152
Roč. 153, October (2019), s. 164-170Number of pages 7 s. Language eng - English Country NL - Netherlands Keywords Particle filtering ; Kernel density estimates ; Convergence rates Subject RIV IN - Informatics, Computer Science OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Method of publishing Limited access Institutional support UIVT-O - RVO:67985807 UT WOS 000480667100023 EID SCOPUS 85068221086 DOI 10.1016/j.spl.2019.06.013 Annotation Bounds on convergence rates of kernel density estimates in particle filtering are specified. The kernel density estimates are shown to be efficient for the Sobolev class of filtering densities. The upper bounds are established using Fourier analysis whilst the lower ones rely on tools of information theory. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2020 Electronic address http://dx.doi.org/10.1016/j.spl.2019.06.013
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